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Applied Supervised Learning with Python
最新章節(jié)：
Chapter 6: Model Evaluation
Machinelearning—theabilityofamachinetogiverightanswersbasedoninputdata—hasrevolutionizedthewaywedobusiness.AppliedSupervisedLearningwithPythonprovidesarichunderstandingofhowyoucanapplymachinelearningtechniquesinyourdatascienceprojectsusingPython.You'llexploreJupyterNotebooks,thetechnologyusedcommonlyinacademicandcommercialcircleswithin-linecoderunningsupport.Withthehelpoffunexamples,you'llgainexperienceworkingonthePythonmachinelearningtoolkit—fromperformingbasicdatacleaningandprocessingtoworkingwitharangeofregressionandclassificationalgorithms.Onceyou’vegraspedthebasics,you'lllearnhowtobuildandtrainyourownmodelsusingadvancedtechniquessuchasdecisiontrees,ensemblemodeling,validation,anderrormetrics.You'llalsolearndatavisualizationtechniquesusingpowerfulPythonlibrariessuchasMatplotlibandSeaborn.Thisbookalsocoversensemblemodelingandrandomforestclassifiersalongwithothermethodsforcombiningresultsfrommultiplemodels,andconcludesbydelvingintocross-validationtotestyouralgorithmandcheckhowwellthemodelworksonunseendata.Bytheendofthisbook,you'llbeequippedtonotonlyworkwithmachinelearningalgorithms,butalsobeabletocreatesomeofyourown!
目錄(51章)
倒序
- 封面
- 版權(quán)頁
- Preface
- About the Book
- Chapter 1 Python Machine Learning Toolkit
- Introduction
- Supervised Machine Learning
- Jupyter Notebooks
- pandas
- Data Quality Considerations
- Summary
- Chapter 2 Exploratory Data Analysis and Visualization
- Introduction
- Summary Statistics and Central Values
- Missing Values
- Distribution of Values
- Relationships within the Data
- Summary
- Chapter 3 Regression Analysis
- Introduction
- Regression and Classification Problems
- Linear Regression
- Multiple Linear Regression
- Autoregression Models
- Summary
- Chapter 4 Classification
- Introduction
- Linear Regression as a Classifier
- Logistic Regression
- Classification Using K-Nearest Neighbors
- Classification Using Decision Trees
- Summary
- Chapter 5 Ensemble Modeling
- Introduction
- Overfitting and Underfitting
- Bagging
- Boosting
- Summary
- Chapter 6 Model Evaluation
- Introduction
- Evaluation Metrics
- Splitting the Dataset
- Performance Improvement Tactics
- Summary
- Appendix
- Chapter 1: Python Machine Learning Toolkit
- Chapter 2: Exploratory Data Analysis and Visualization
- Chapter 3: Regression Analysis
- Chapter 4: Classification
- Chapter 5: Ensemble Modeling
- Chapter 6: Model Evaluation 更新時間：2021-06-11 13:45:00
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